





'''simple docstring'''

from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


a   	=    logging.get_logger(__name__)

a   	=    {
    "deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}







class __a			( _snake_case      ):
   __UpperCamelCase :			Optional[Any]						   =						'perceiver'





   def __init__(       self					:      Any					,lowerCamelCase					:      int=256					,lowerCamelCase					:      Tuple=1280					,lowerCamelCase					:      List[Any]=768					,lowerCamelCase					:      str=1					,lowerCamelCase					:      Dict=26					,lowerCamelCase					:      Any=8					,lowerCamelCase					:      Optional[Any]=8					,lowerCamelCase					:      List[Any]=None					,lowerCamelCase					:      List[str]=None					,lowerCamelCase					:      List[str]="kv"					,lowerCamelCase					:      int=1					,lowerCamelCase					:      Any=1					,lowerCamelCase					:      Any="gelu"					,lowerCamelCase					:      int=0.1					,lowerCamelCase					:      Optional[Any]=0.02					,lowerCamelCase					:      Union[str, Any]=1E-1_2					,lowerCamelCase					:      Any=True					,lowerCamelCase					:      Dict=262					,lowerCamelCase					:      int=2048					,lowerCamelCase					:      Any=56					,lowerCamelCase					:      Dict=[368, 496]					,lowerCamelCase					:      List[str]=16					,lowerCamelCase					:      Dict=1920					,lowerCamelCase					:      Any=16					,lowerCamelCase					:      Dict=[1, 16, 224, 224]					,**lowerCamelCase					:      List[str]					,):

      '''simple docstring'''

      super().__init__(**lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							num_latents
      __SCREAMING_SNAKE_CASE   				=							d_latents
      __SCREAMING_SNAKE_CASE   				=							d_model
      __SCREAMING_SNAKE_CASE   				=							num_blocks
      __SCREAMING_SNAKE_CASE   				=							num_self_attends_per_block
      __SCREAMING_SNAKE_CASE   				=							num_self_attention_heads
      __SCREAMING_SNAKE_CASE   				=							num_cross_attention_heads
      __SCREAMING_SNAKE_CASE   				=							qk_channels
      __SCREAMING_SNAKE_CASE   				=							v_channels
      __SCREAMING_SNAKE_CASE   				=							cross_attention_shape_for_attention
      __SCREAMING_SNAKE_CASE   				=							self_attention_widening_factor
      __SCREAMING_SNAKE_CASE   				=							cross_attention_widening_factor
      __SCREAMING_SNAKE_CASE   				=							hidden_act
      __SCREAMING_SNAKE_CASE   				=							attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							initializer_range
      __SCREAMING_SNAKE_CASE   				=							layer_norm_eps
      __SCREAMING_SNAKE_CASE   				=							use_query_residual
      # masked language modeling attributes
      __SCREAMING_SNAKE_CASE   				=							vocab_size
      __SCREAMING_SNAKE_CASE   				=							max_position_embeddings
      # image classification attributes
      __SCREAMING_SNAKE_CASE   				=							image_size
      # flow attributes
      __SCREAMING_SNAKE_CASE   				=							train_size
      # multimodal autoencoding attributes
      __SCREAMING_SNAKE_CASE   				=							num_frames
      __SCREAMING_SNAKE_CASE   				=							audio_samples_per_frame
      __SCREAMING_SNAKE_CASE   				=							samples_per_patch
      __SCREAMING_SNAKE_CASE   				=							output_shape







class __a			( _snake_case      ):





   @property
   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      if self.task == "multiple-choice":
         __SCREAMING_SNAKE_CASE   				=							{0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
         __SCREAMING_SNAKE_CASE   				=							{0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""inputs""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ]     )





   @property
   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      return 1E-4





   def   UpperCAmelCase__  (       self					:      Union[str, Any]					,lowerCamelCase					:      Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]					,lowerCamelCase					:      int = -1					,lowerCamelCase					:      int = -1					,lowerCamelCase					:      int = -1					,lowerCamelCase					:      bool = False					,lowerCamelCase					:      Optional[TensorType] = None					,lowerCamelCase					:      int = 3					,lowerCamelCase					:      int = 40					,lowerCamelCase					:      int = 40					,):

      '''simple docstring'''

      if isinstance(lowerCamelCase					,lowerCamelCase     ):
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         __SCREAMING_SNAKE_CASE   				=							compute_effective_axis_dimension(
             lowerCamelCase					,fixed_dimension=OnnxConfig.default_fixed_batch					,num_token_to_add=0     )
         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         __SCREAMING_SNAKE_CASE   				=							preprocessor.num_special_tokens_to_add(lowerCamelCase     )
         __SCREAMING_SNAKE_CASE   				=							compute_effective_axis_dimension(
             lowerCamelCase					,fixed_dimension=OnnxConfig.default_fixed_sequence					,num_token_to_add=lowerCamelCase     )
         # Generate dummy inputs according to compute batch and sequence
         __SCREAMING_SNAKE_CASE   				=							[""" """.join(["""a"""]     ) * seq_length] * batch_size
         __SCREAMING_SNAKE_CASE   				=							dict(preprocessor(lowerCamelCase					,return_tensors=lowerCamelCase     )     )
         __SCREAMING_SNAKE_CASE   				=							inputs.pop("""input_ids"""     )
         return inputs
      elif isinstance(lowerCamelCase					,lowerCamelCase     ) and preprocessor.model_input_names[0] == "pixel_values":
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         __SCREAMING_SNAKE_CASE   				=							compute_effective_axis_dimension(lowerCamelCase					,fixed_dimension=OnnxConfig.default_fixed_batch     )
         __SCREAMING_SNAKE_CASE   				=							self._generate_dummy_images(lowerCamelCase					,lowerCamelCase					,lowerCamelCase					,lowerCamelCase     )
         __SCREAMING_SNAKE_CASE   				=							dict(preprocessor(images=lowerCamelCase					,return_tensors=lowerCamelCase     )     )
         __SCREAMING_SNAKE_CASE   				=							inputs.pop("""pixel_values"""     )
         return inputs
      else:
         raise ValueError(
             """Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."""     )









"""simple docstring"""



import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class    __a						( __snake_case					,			unittest.TestCase							):
				lowerCamelCase     :       Any			       =GPTSanJapaneseTokenizer
				lowerCamelCase     :       List[str]			       =False
				lowerCamelCase     :       List[str]			       ={'do_clean_text': False, 'add_prefix_space': False}







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					super().setUp()

					# fmt: off
					lowerCAmelCase_      							=			['''こん''', '''こんに''', '''にちは''', '''ばんは''', '''世界,㔺界''', '''、''', '''。''', '''<BR>''', '''<SP>''', '''<TAB>''', '''<URL>''', '''<EMAIL>''', '''<TEL>''', '''<DATE>''', '''<PRICE>''', '''<BLOCK>''', '''<KIGOU>''', '''<U2000U2BFF>''', '''<|emoji1|>''', '''<unk>''', '''<|bagoftoken|>''', '''<|endoftext|>''']
					# fmt: on
					lowerCAmelCase_      							=			{'''emoji''': {'''\ud83d\ude00''': '''<|emoji1|>'''}, '''emoji_inv''': {'''<|emoji1|>''': '''\ud83d\ude00'''}}  # 😀
					lowerCAmelCase_      							=			{'''unk_token''': '''<unk>'''}

					lowerCAmelCase_      							=			os.path.join(self.tmpdirname      ,	VOCAB_FILES_NAMES['''vocab_file''']   )
					lowerCAmelCase_      							=			os.path.join(self.tmpdirname      ,	VOCAB_FILES_NAMES['''emoji_file''']   )
					with open(self.vocab_file      ,	'''w'''      ,	encoding='''utf-8'''   ) as vocab_writer:
						vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]   )   )
					with open(self.emoji_file      ,	'''w'''   ) as emoji_writer:
						emoji_writer.write(json.dumps(UpperCAmelCase   )   )







				def      lowerCamelCase_     ( self      ,	**UpperCAmelCase   ):

					'''simple docstring'''






					kwargs.update(self.special_tokens_map   )
					return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname      ,	**UpperCAmelCase   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			'''こんにちは、世界。 \nこんばんは、㔺界。😀'''
					lowerCAmelCase_      							=			'''こんにちは、世界。 \nこんばんは、世界。😀'''
					return input_text, output_text







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_							,	lowerCAmelCase_      							=			self.get_input_output_texts(UpperCAmelCase   )
					lowerCAmelCase_      							=			tokenizer.encode(UpperCAmelCase      ,	add_special_tokens=UpperCAmelCase   )
					lowerCAmelCase_      							=			tokenizer.decode(UpperCAmelCase      ,	clean_up_tokenization_spaces=UpperCAmelCase   )
					return text, ids







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					pass  # TODO add if relevant







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					pass  # TODO add if relevant







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					pass  # TODO add if relevant







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.get_tokenizer()

					# Testing tokenization
					lowerCAmelCase_      							=			'''こんにちは、世界。　こんばんは、㔺界。'''
					lowerCAmelCase_      							=			['''こん''', '''にちは''', '''、''', '''世界''', '''。''', '''<SP>''', '''こん''', '''ばんは''', '''、''', '''㔺界''', '''。''']
					lowerCAmelCase_      							=			tokenizer.tokenize(UpperCAmelCase   )
					self.assertListEqual(UpperCAmelCase      ,	UpperCAmelCase   )

					# Testing conversion to ids without special tokens
					lowerCAmelCase_      							=			[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
					lowerCAmelCase_      							=			tokenizer.convert_tokens_to_ids(UpperCAmelCase   )
					self.assertListEqual(UpperCAmelCase      ,	UpperCAmelCase   )

					# Testing conversion to ids with special tokens
					lowerCAmelCase_      							=			tokens + [tokenizer.unk_token]
					lowerCAmelCase_      							=			[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
					lowerCAmelCase_      							=			tokenizer.convert_tokens_to_ids(UpperCAmelCase   )
					self.assertListEqual(UpperCAmelCase      ,	UpperCAmelCase   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.get_tokenizer()

					# Testing tokenization
					lowerCAmelCase_      							=			'''こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'''
					lowerCAmelCase_      							=			'''こんにちは、、、、世界。こんばんは、、、、世界。'''
					lowerCAmelCase_      							=			tokenizer.encode(UpperCAmelCase   )
					lowerCAmelCase_      							=			tokenizer.decode(UpperCAmelCase   )
					self.assertEqual(UpperCAmelCase      ,	UpperCAmelCase   )







				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''   )

					# Testing tokenization
					lowerCAmelCase_      							=			'''こんにちは、世界。'''
					lowerCAmelCase_      							=			'''こんばんは、㔺界。😀'''
					lowerCAmelCase_      							=			'''こんにちは、世界。こんばんは、世界。😀'''
					lowerCAmelCase_      							=			tokenizer.encode(prefix_text + input_text   )
					lowerCAmelCase_      							=			tokenizer.encode(''''''      ,	prefix_text=prefix_text + input_text   )
					lowerCAmelCase_      							=			tokenizer.encode(UpperCAmelCase      ,	prefix_text=UpperCAmelCase   )
					lowerCAmelCase_      							=			tokenizer.decode(UpperCAmelCase   )
					lowerCAmelCase_      							=			tokenizer.decode(UpperCAmelCase   )
					lowerCAmelCase_      							=			tokenizer.decode(UpperCAmelCase   )
					self.assertEqual(UpperCAmelCase      ,	UpperCAmelCase   )
					self.assertEqual(UpperCAmelCase      ,	UpperCAmelCase   )
					self.assertEqual(UpperCAmelCase      ,	UpperCAmelCase   )







				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''   )

					# Testing tokenization
					lowerCAmelCase_      							=			'''こんにちは、世界。'''
					lowerCAmelCase_      							=			'''こんばんは、㔺界。😀'''

					lowerCAmelCase_      							=			len(tokenizer.encode(UpperCAmelCase   )   ) - 2
					lowerCAmelCase_      							=			len(tokenizer.encode(UpperCAmelCase   )   ) - 2

					lowerCAmelCase_      							=			[1] + [0] * (len_prefix + len_text + 1)
					lowerCAmelCase_      							=			[1] * (len_prefix + len_text + 1) + [0]
					lowerCAmelCase_      							=			[1] + [1] * (len_prefix) + [0] * (len_text + 1)

					lowerCAmelCase_      							=			tokenizer(prefix_text + input_text   ).token_type_ids
					lowerCAmelCase_      							=			tokenizer(''''''      ,	prefix_text=prefix_text + input_text   ).token_type_ids
					lowerCAmelCase_      							=			tokenizer(UpperCAmelCase      ,	prefix_text=UpperCAmelCase   ).token_type_ids
					self.assertListEqual(UpperCAmelCase      ,	UpperCAmelCase   )
					self.assertListEqual(UpperCAmelCase      ,	UpperCAmelCase   )
					self.assertListEqual(UpperCAmelCase      ,	UpperCAmelCase   )







				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''   )

					lowerCAmelCase_      							=			tokenizer.encode('''あンいワ'''   )
					lowerCAmelCase_      							=			tokenizer.encode(''''''      ,	prefix_text='''あンいワ'''   )
					lowerCAmelCase_      							=			tokenizer.encode('''いワ'''      ,	prefix_text='''あン'''   )

					self.assertEqual(tokenizer.decode(UpperCAmelCase   )      ,	tokenizer.decode(UpperCAmelCase   )   )
					self.assertEqual(tokenizer.decode(UpperCAmelCase   )      ,	tokenizer.decode(UpperCAmelCase   )   )
					self.assertNotEqual(UpperCAmelCase      ,	UpperCAmelCase   )
					self.assertNotEqual(UpperCAmelCase      ,	UpperCAmelCase   )
					self.assertEqual(x_token_a[1]      ,	x_token_a[-1]   )  # SEG token
					self.assertEqual(x_token_a[1]      ,	x_token_a[3]   )  # SEG token







				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''   )

					lowerCAmelCase_      							=			[['''武田信玄''', '''は、'''], ['''織田信長''', '''の配下の、''']]
					lowerCAmelCase_      							=			tokenizer(UpperCAmelCase      ,	padding=UpperCAmelCase   )
					lowerCAmelCase_      							=			tokenizer.batch_encode_plus(UpperCAmelCase      ,	padding=UpperCAmelCase   )

					# fmt: off
					lowerCAmelCase_      							=			[[3_5993, 8640, 2_5948, 3_5998, 3_0647, 3_5675, 3_5999, 3_5999], [3_5993, 1_0382, 9868, 3_5998, 3_0646, 9459, 3_0646, 3_5675]]
					lowerCAmelCase_      							=			[[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
					lowerCAmelCase_      							=			[[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
					# fmt: on
					self.assertListEqual(x_token.input_ids      ,	UpperCAmelCase   )
					self.assertListEqual(x_token.token_type_ids      ,	UpperCAmelCase   )
					self.assertListEqual(x_token.attention_mask      ,	UpperCAmelCase   )
					self.assertListEqual(x_token_a.input_ids      ,	UpperCAmelCase   )
					self.assertListEqual(x_token_a.token_type_ids      ,	UpperCAmelCase   )
					self.assertListEqual(x_token_a.attention_mask      ,	UpperCAmelCase   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					pass







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					pass